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A framework for multiplex imaging optimization and reproducible analysis

View ORCID ProfileJennifer Eng, Elmar Bucher, Zhi Hu, Ting Zheng, Summer L. Gibbs, Koei Chin, Joe W. Gray
doi: https://doi.org/10.1101/2021.11.29.470281
Jennifer Eng
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
2Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201 USA
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  • ORCID record for Jennifer Eng
Elmar Bucher
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
2Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201 USA
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Zhi Hu
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
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Ting Zheng
3Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR 97201 USA
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Summer L. Gibbs
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
2Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201 USA
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Koei Chin
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
2Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201 USA
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Joe W. Gray
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97201 USA
2Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201 USA
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  • For correspondence: grayjo@ohsu.edu
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Abstract

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, jinxif, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 30, 2021.
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A framework for multiplex imaging optimization and reproducible analysis
Jennifer Eng, Elmar Bucher, Zhi Hu, Ting Zheng, Summer L. Gibbs, Koei Chin, Joe W. Gray
bioRxiv 2021.11.29.470281; doi: https://doi.org/10.1101/2021.11.29.470281
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A framework for multiplex imaging optimization and reproducible analysis
Jennifer Eng, Elmar Bucher, Zhi Hu, Ting Zheng, Summer L. Gibbs, Koei Chin, Joe W. Gray
bioRxiv 2021.11.29.470281; doi: https://doi.org/10.1101/2021.11.29.470281

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